code: def exponential(
      random_state,
      scale=1.0,
      size=None,
      chunk_size=None,
      gpu=None,
      dtype=None,
  ):
      r"""
      Draw samples from an exponential distribution.
  
      Its probability density function is
  
      .. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),
  
      for ``x > 0`` and 0 elsewhere. :math:`\beta` is the scale parameter,
      which is the inverse of the rate parameter :math:`\lambda = 1/\beta`.
      The rate parameter is an alternative, widely used parameterization
      of the exponential distribution [3]_.
  
      The exponential distribution is a continuous analogue of the
      geometric distribution.  It describes many common situations, such as
      the size of raindrops measured over many rainstorms [1]_, or the time
      between page requests to Wikipedia [2]_.
  
      Parameters
      ----------
      scale : float or array_like of floats
          The scale parameter, :math:`\beta = 1/\lambda`.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``scale`` is a scalar.  Otherwise,
          ``np.array(scale).size`` samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized exponential distribution.
  
      References
      ----------
      .. [1] Peyton Z. Peebles Jr., "Probability, Random Variables and
             Random Signal Principles", 4th ed, 2001, p. 57.
      .. [2] Wikipedia, "Poisson process",
             http://en.wikipedia.org/wiki/Poisson_process
      .. [3] Wikipedia, "Exponential distribution",
             http://en.wikipedia.org/wiki/Exponential_distribution
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .exponential(handle_array(scale), size=(0,))
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorExponential(seed=seed, size=size, gpu=gpu, dtype=dtype)
      return op(scale, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/exponential.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: exponential(random_state, scale, size, chunk_size, gpu, dtype) and returns: op